Keg cleaning is essential for hygiene but requires substantial water, energy, and chemicals. Process analytical technology (PAT) could adapt cleaning to actual contamination levels. In adjacent sectors, PAT-based cleaning has demonstrated water savings up to 35% and energy reductions exceeding 50%. However, despite this potential, we identified no documented sensor-based keg cleaning implementation in the literature or among experts, and therefore no comparable savings figures for kegs. To understand this gap, we conducted semi-structured interviews with 14 experts from breweries, OEMs, suppliers and research. The technology-organisation-environment framework served as the principal analytical lens, with transition and collective-action concepts used to interpret how the three domains interact. Our findings reveal a striking paradox. Although PAT-based cleaning is established in adjacent industries, strong cross-stakeholder convergence emerged around four enablers still missing, namely validated pilot projects, transparent sensor concepts to dispel black-box concerns, flexible validation standards, and targeted training to bridge automation and data-analytics gaps. The interviewees located the barriers not in technology but in coordination, validation, and governance across the value chain. Overall, the study suggests non-adoption as a possible coordination lock-in, without claiming a sector-wide mechanism. Thus, it provides a first multistakeholder analysis of barriers to sensor-based keg cleaning.
This study demonstrates the successful synthesis of zinc oxide nanoparticles (ZnO NPs) on acrylic (PAN) fabrics via gamma radiation. PAN fabrics were padded with an alkaline zinc nitrate solution of 0.1 and 0.2 M, before treatment with 2% w/v of poly(vinyl alcohol) (PVA). Gamma radiation of 10 kGy induced the formation of ZnO NPs and PVA crosslinking. Scanning electron microscopy revealed spherical ZnO NPs with an average diameter of 47 ± 16 nm, while X-ray diffraction confirmed their wurtzite structure. Inductively coupled plasma–optical emission spectrometry showed that zinc content in the treated fabrics was lower for fabrics treated with PVA or with a higher zinc nitrate concentration. However, a PVA-coated fabric exhibited comparable antibacterial efficiency to that without PVA coating but with improved wash durability. The fabric treated with 0.1 M zinc nitrate and 2% w/v PVA contained 1.11 wt% zinc, with >95% bacterial reduction after 30 washing cycles and excellent ultraviolet (UV) blocking ability (UPF 50+). Although changes in tensile strength and elongation at break were observed, the mechanical properties remained comparable, indicating no severe deterioration. In summary, this is an environmentally benign and safe approach to producing antibacterial and UV-protective fabrics for outdoor and healthcare applications.
The adoption of electric vehicles in emerging and non-metropolitan regions depends on functional suitability, affordability, technical trust, charging conditions, and local support capacity. This study analyzes consumer needs, perceptions, and attitudes toward accessible electric vehicles in southern Jalisco, Mexico, using the Olinia mini electric vehicle as a case study. A cross-sectional survey was administered face-to-face to 640 adults across urban and semi-urban municipalities. The analysis combined descriptive statistics, reliability testing, exploratory factor analysis, ordered logistic regression models, user segmentation, and qualitative coding of open-ended responses. Results show that most daily mobility needs are compatible with Olinia’s proposed technical concept, particularly its approximate 100 km range and urban-use orientation. However, acceptance is conditional. Functional fit was the strongest positive predictor of willingness to consider and purchase the vehicle, while perceived risk related to reliability and driving range reduced willingness to adopt. The segmentation analysis identified four user profiles with different information, financing, trust, and service needs. The study contributes by translating consumer perceptions into engineering and infrastructure requirements, showing that adoption will depend on real-world range testing, safe domestic charging, local technical capacity, and credible after-sales support.
Beyond thermal performance, the long-term viability of novel cryogenic insulation technologies is contingent upon several factors, such as low cost, durability, minimal or no maintenance, among others. Therefore, finding the optimal insulation concept involves trade-offs among these attributes, especially during early development stages. To assess the functional requirements of novel insulation materials and concepts, this paper presents a systematic approach based on the Quality Function Deployment (QFD) method. A simplified House of Quality (HoQ) model, representing the first phase of QFD, was used to propose and link technical features that meet the identified needs of customers. A survey-based approach was employed to populate the HoQ, leading to the identification of eighteen functional requirements and twenty-two technical features, which were subsequently ranked by the respondents’ perceived level of their importance. The results from this study provide a structured decision-support framework and an integrative list of requirements to support improvements in insulation system design. Furthermore, the study proposes strategies for optimizing the development of next-generation hydrogen storage technologies.
Illegal poaching in remote forest regions poses a critical threat to global biodiversity, with traditional monitoring methods such as manual patrols and camera traps proving inadequate due to delayed response times and limited coverage. This paper presents a novel low-power, AI-Powered Edge-Based Gunshot Detection (AI-PEGD) designed for deployment in harsh forest environments. The system employs edge AI computing, utilizing a Raspberry Pi Pico microcontroller interfaced with an INMP441 digital MEMS microphone for real-time audio acquisition and processing. A lightweight TensorFlow Lite model, optimized for microcontroller deployment, performs on-device gunshot classification with minimal power consumption. Detection alerts are transmitted wirelessly via nRF24L01 modules to a centralized STM32-based receiver for event logging and monitoring. The hardware is protected by a biodegradable weather-resistant enclosure fabricated from coconut fiber-epoxy composites. Field testing demonstrates the system’s effectiveness in detecting gunshots with 94.2% accuracy in Line of Sight with less than 200 ms response time and system also tested in forest region and observed the accuracy of 91% while maintaining sustainable operation in remote forest environments. This interdisciplinary approach combines machine learning, embedded systems, and sustainable materials to provide a scalable solution for wildlife protection in the Indian context.
The transport sector accounts for a significant amount of greenhouse gas emissions globally, with road transport contributing approximately 70% of emissions. This has prompted efforts globally to find sustainable solutions for transportation. Fuel cell vehicles (FCVs) have gained traction as a promising alternative because of their long range, strong performance, and rapid refuelling. This research presents a systematic review of the sustainability performance of FCVs according to the triple bottom line of sustainability, following the PRISMA methodology and reviewing studies conducted between 2015 and 2025. The findings suggest that FCVs can rival or outperform the environmental performance of other types of vehicles in long-range, heavy-duty, and public transport applications. However, the environmental performance of FCVs is highly dependent on hydrogen production pathways. From an economic perspective, the viability of FCVs is constrained by the vehicle and infrastructure costs. Social sustainability is examined to a lesser extent compared with other spheres of sustainability. Mapping the extracted papers against the UN Sustainable Development Goals reveals that most studies are aligned with climate action, clean energy, and sustainable city goals, and limited alignment with goals such as social equity and biodiversity is observed.
This paper presents a virtual–physical hybrid digital twin for sustainable operation and optimization of an integrated catalytic purification and CO₂ capture system. The twin continuously links a real emission treatment installation with its digital representation to improve energy efficiency, emission reduction, and adaptability under variable operating conditions. The framework integrates physics-based models of electrostatic particulate removal, catalytic conversion of gaseous pollutants, and thermodynamic CO₂ capture in a unified virtual environment. Data-driven components enable real-time synchronization between the physical system and its digital counterpart for transient regimes and load fluctuations. Validation against laboratory and operational data shows strong agreement between virtual and physical behaviour, including particulate removal accuracy of 94.7%, catalytic conversion deviations below 2.3%, and CO₂ capture performance exceeding 92%. Integrated optimization resulted in a 17.2% reduction in energy consumption compared to conventional operation. The results demonstrate the suitability of the proposed digital twin for improving sustainability and environmental performance of industrial emission purification and decarbonization systems.
The textile industry generates wastewater containing harmful heavy metals, including lead (Pb), while Thailand's agricultural sector burns sugarcane leaves open-field, causing significant air pollution. Sugarcane leaves can be turned into activated carbon to clean wastewater and manage agricultural leftovers. A cradle-to-gate life cycle assessment (LCA) evaluates the environmental impact of sugarcane leaf-derived activated carbon (SLAC) produced using various chemical activators (H₃PO₄, ZnCl₂ and KOH) and pyrolysis temperatures (400 °C and 600 °C). The CML-IA baseline method is used in SimaPro to analyse laboratory-scale production data. The results revealed that activation chemistry and heat intensity greatly affect environmental impacts, with energy use and chemical production being engineering hotspots. ZnCl₂ activation at 400 °C had the lowest global warming potential, toxicity and resource depletion among the investigated processes. Raising the activation temperature to 600 °C raises the environmental load without improving sustainability. According to the sensitivity analysis, energy efficiency and product output are the most important design characteristics, while renewable electricity integration significantly minimises climate consequences. The findings provide quantitative engineering guidelines for low-impact activation methodologies and operating conditions, enabling the development of sustainable agricultural residue adsorbent materials for decentralised textile wastewater treatment systems.
Recent global shocks have significantly impacted India's Energy-Storage-Systems (ESS), revealing vulnerabilities in supply chains, investment flow, and market stability, while underscoring the urgent requirement for resilience and sustainability in this vital aspect of renewable integration. The study is grounded in Supply-Chain-Resilience-Theory and Dynamic-Capabilities-Theory and focuses on India's ESS, with potential for generalisation to other emerging economies. ESS has been acknowledged for its capacity to facilitate clean energy transitions; yet studies about its adaptability to unexpected global shocks remain scarce, creating a significant gap. To address this gap, the study combines SAP-LAP (Situation-Actor-Process-Learning-Action-Performance) model with Triple-Bottom-Line (TBL) framework. Interpretive-Ranking-Process (IRP) is utilised to systematically prioritise actions according to their impact on performance outcomes. Findings indicate that the foremost priority for sustaining growth is government and financial support, accomplished by stakeholder participation, technological advancement and training, prevention of CO2 emissions, and enhanced resource efficiency. It further emphasises the necessity for institutional support, industry collaboration, and technological innovation to work in tandem to achieve resilience and sustainable growth. This research enhances theoretical understanding by establishing connections between sustainability, resilience, and performance within the ESS framework. It offers practical implications for policymakers, businesses, and researchers by providing a structured roadmap to strengthen long-term sustainability under future global disruptions.
With the growing demand from consumers seeking alternative solutions to pest management, ultrasonic pest repellents are becoming more commonplace in households. This review reports the performance of ultrasonic technology against 14 common pests based on peer-reviewed experimental and field studies. The reported effects are species specific, such as high larval mortality rates (85%–100%) for aquatic mosquitoes or transient spatial repellency for rodents. These responses are limited to controlled laboratory studies. For mammalian pests, behavioural interference is nullified by rapid cognitive habituation for 3–7 days. For insects, commercially available devices (20–100 kHz) fall well below (>160 dB) behavioural threshold intensities known to evoke avoidance in even the most susceptible pests such as cockroaches. This lack of effect was further demonstrated in field studies, which showed no ability to measurably decrease pest populations. The production of these devices contributes to the growing issue of electronic waste. Instead of considering a circular economy, ultimately, regulators should advocate for a Minimum Evidence Standard for Market Approval. Finally, repelling pests with ultrasonic devices should not be performed as a standalone solution. Avenues for potentially useful, narrowly targeted, stage-specific applications may exist as part of an Integrated Pest Management (IPM) program.
Industrial manufacturing faces increasing pressure to enhance sustainability performance, yet companies struggle to effectively translate environmental goals into actionable strategies. To address this challenge, this study introduces the Process Sustainability Impact Deployment (PSID) methodology, a structured decision-support framework for sustainable factory planning. The PSID methodology builds upon the principles of Quality Function Deployment (QFD) and integrates sustainability considerations into early-stage decision-making processes. By combining sustainability requirements, process adaptability assessments, and impact evaluations, PSID helps planners identify critical process steps that offer potential for environmental improvements. The methodology was applied in a real-world use case at MAN Truck & Bus SE, where a battery production facility was analyzed to identify key sustainability drivers. Results revealed that electrification, energy efficiency, and circularity emerged as top sustainability priorities. Process steps such as ‘gap filling’, ‘rotation’, and ‘base & screwing’ were identified as sustainability hotspots requiring strategic interventions. Conversely, adaptable steps like ‘insulation’, ‘impact protection’, and ‘low voltage wiring’ offered immediate improvement potential. The PSID methodology effectively balances sustainability impact with practical feasibility, supporting both short-term improvements and long-term strategic measures.
Engineers’ decisions can have significant impacts on society and the environment. In this sense, engineering undergraduate courses must prepare their students to work towards sustainable development. Considering the relevance and complexity of engineering education for sustainable development (EESD), models for assessing the maturity of this insertion can be of great importance for implementing and improving this insertion. In this sense, this paper develops a model for assessing the maturity level of EESD. The Delphi method was employed for this purpose, reaching a consensus in the third round. The improved, validated model was subsequently applied to evaluate 29 engineering programs. The proposed model delineates six maturity levels for assessment. The model can be used by coordinators of engineering courses to assess their current stage and plan improvements towards EESD. The model proposed can be used as a comprehensive guide for advancing sustainability integration, as it offers a structured approach to curriculum development, targeted faculty training, and cohesive strategies for interconnected elements. Policymakers can craft evidence-based policies fostering responsiveness, supporting diverse sustainability dimensions, and encouraging institutions to innovate. The results can also guide research on how to tailor sustainability integration in engineering programs.
Limited access to nanomaterials synthesis infrastructure constrains resource-limited institutions, where commercial chemical vapor deposition (CVD) systems are often unaffordable. This study reports a reuse-oriented tubular CVD setup and a screening-level techno-economic analysis (TEA) framework for frugal carbon nanotubes (CNTs) production. Hardware capital expenditure (CAPEX) amounted to CAPEXP=4,310.00 USD (prototype) and CAPEXR=7,046.17 USD (replication). As a case study, acetylene decomposition over Fe-Co/CaCO3 at atmospheric pressure produced raw CNTs (RCNTs); subsequent acid leaching yielded purified CNTs (PCNTs). Characterization via scanning electron microscopy (SEM), transmission electron microscopy (TEM), Raman spectroscopy, and X-ray diffraction (XRD) confirmed a filamentous/tubular carbon morphology and the strong attenuation of CaCO3 reflections after purification, consistent with effective removal of the bulk catalyst support. Using the dry PCNTs mass as the functional unit and local 2025 prices, the TEA estimated a unit production cost of Cp,OPEX=517.87 USD/g and a levelized cost of Cp,LC=606.59 USD/g, with operating expenditures (OPEX) dominating under baseline utilization. This approach provides a practical template to quantify cost drivers for in-house CNTs production in resource-constrained academic settings.
This study presents a novel eco-smart composite fabricated entirely from recycled PLA waste, reclaimed aerospace-grade carbon fibers, and multifunctional inorganic fillers recovered from desalination brine. Brine-derived salts were obtained via an electrocoagulation-modified Solvay hybrid process and incorporated into a PLA matrix containing 10 wt% carbon fibers and 1-5 wt% mineral fillers. Composites were produced through melt compounding and injection molding and evaluated for mechanical, thermal, acoustic, moisture, and fire performance. The optimized 85% PLA-10% CF-5% brine formulation achieved a thermal resistance of 5.59 m center dot K/W (thermal conductivity: 0.179 W/(m center dot K)), alongside an 88% reduction in water absorption (0.086%) compared with neat PLA. Fire performance improved significantly, reaching a UL-94 V-2 rating and a limiting oxygen index (LOI) of 26%. TGA revealed enhanced thermal stability and increased char yield, with residual mass rising from similar to 1.2 wt% for neat PLA to similar to 5.3 wt% at 700 degrees C. FTIR analysis confirmed filler incorporation primarily through physical interfacial interactions. Acoustic testing showed a peak absorption coefficient of 0.93 at 2 kHz. Tensile properties improved up to 3 wt% brine loading, beyond which filler agglomeration reduced uniformity. This triple-waste composite demonstrates a scalable circular-economy pathway for sustainable, multifunctional building materials. [GRAPHICS] .
The growing emphasis on environmental accountability necessitates comprehensive sustainability assessments in horticultural food supply chains. This study evaluated the carbon footprint of the cauliflower supply chain using a circular tiered hybrid life cycle assessment (CrTH-LCA) framework, integrated with time-series forecasting of emissions. The total carbon footprint was estimated at 28,454.06 kg CO2-e/ha, with fertilizers (79.84%) and household waste (8.35%) identified as major emission hotspots. Waste valorisation demonstrated significant carbon offset potential, generating $227.82/ha from methane recovery and $50,707.35/ha from farmyard manure production. Monte Carlo sensitivity analysis revealed fertilizers and household waste as the most influential factors in total emissions. Deep learning-based forecasting (GRU model) achieved the highest accuracy (MAE = 66.85; RMSE = 82.23), emphasizing the growing carbon challenge associated with increasing human demand. Six-year projections suggest that community-based waste valorisation could yield a return on investment of 785.23% with a payback period of 8.5 months. Overall, this study proposes a replicable, data-driven framework integrating LCA, circular waste management, and carbon forecasting to support carbon-informed decision-making. Aligned with SDGs 2, 12, and 13, the findings underscore the need for policy reforms, carbon financing, and targeted incentives to promote waste-to-energy initiatives and transition toward a low-carbon horticultural supply chain.
‘Waste’ has evolved from a local civic problem into an intertwined issue in global policy and business practices due to its adverse impact on climate change, natural resources depletion and overall quality of life. Until 2017, China had been the world's largest importer of consumer waste from the Global North. However, China's implementation of the National Sword Policy, which imposed a ban on the import of various categories of consumer waste, abruptly ended its dominance in global waste imports. In this context, numerous legislations have been passed in the Global North to tackle waste management-related issues. At the same time, significant volumes of waste has been directed to other developing countries such as India, placing additional pressure on an already overburdened waste management system. The present work (i) reviewed and analysed major policies introduced in the Global North post-National Sword Policy era and their impacts on India's waste imports and waste management and (ii) developed an integrated AID framework, comprises Awareness development (A), incentives/incentivisation (I) and data-based governance (D) to strengthen waste management and circular economy policies within the Indian context. The application of the AID framework in real-time waste management is highlighted through relevant case studies from across India.
In today's competitive market environment, organizations must meet regulatory requirements, satisfy customer expectations, achieve operational efficiency and address sustainability goals. Green Lean Six Sigma (GLSS) offers an integrated methodology to support this balance, yet its practical application - particularly within supply chain operations - remains limited. This study proposes and validates a seven-stage, twenty-step GLSS implementation framework, grounded in dynamic capabilities and stakeholder theories, aimed at improving customer, operational and environmental performance. The research followed three phases: framework conception, testing, and validation. A literature review guided the framework's development, which integrates essential improvement tools. The framework was applied to supply chain processes in a gear motor assembly company to evaluate its feasibility and impact. The implementation led to substantial performance gains: On-time delivery, customers improved from a peak of 36% to 75%, spare parts stockouts dropped from more than 6.4% to below 2%, customer complaints declined to one in the last three months and CO2 emissions from imported parts transport were reduced by over 75%. The case study also identified five critical success factors essential for effective GLSS adoption. Compared to existing approaches, the proposed framework is theory-based and empirically validated, offering a robust guide for enhancing supply chain performance.
This research investigates the utilisation of red mud and fly ash in developing controlled low-strength materials (CLSM). A comprehensive evaluation of the engineering properties and shear wave velocity was conducted, and various strength and flow phenomenological models were derived. Flowability, a critical parameter, exhibited a decrease with an increase in red mud content, and most of the mixes satisfied the ACI-229R recommendations, while the compressive strength peaked at 28th day before gradually decreasing by 15%-20% of the 28th day compressive strength towards the 90th day. The split tensile strength of the CLSM samples were between 0.36 and 0.5 MPa, while the water absorption was approximately 13% for all the samples tested at the age of 28 days. The shear wave velocity of the CLSM samples was determined to be 390 m/s, which corresponds to the stiff soil classification. The setting time of CLSM samples was found to be between 210 and 267 min, indicating a quick setting mix. The fresh and hardened density of the CLSM samples were within the ACI-229 R stipulation. The sorptivity values of the mixes indicate that they have a dense internal structure and perform better than a well-compacted soil sample.
This study evaluates immersion cooling strategies using hydrogen-based coolants for Li-ion battery thermal management in hybrid electric vehicles using conjugate CFD across seven fluids and a data-driven multi-objective (SVR metamodel and evolutionary) optimization. In the context of vehicle lightweighting and energy savings, the operating envelopes spanned 1-2C discharge rates and 0.1-20 m center dot s(-1) coolant velocities. Liquid hydrogen (LH2) delivered outstanding thermal uniformity, keeping the module temperature difference below 0.05 degrees C while reducing the pumping power dramatically, by similar to 99.7%. Gaseous H-2 and its blend with helium are also highly competitive at high speeds (similar to 20 m center dot s(-1)), maintaining safe module temperatures (similar to 300.7 K at 2 C). For instance, a 40:60 H-2-He mixture significantly increases the safety margins while retaining nearly identical cooling performance to that of pure hydrogen, offering a pragmatic safety performance compromise. Design optimization indicates that modest geometric and flow modifications reduce the maximum module temperature from 28.74 to 28.56 degrees C while decreasing the inter-cell temperature difference from 0.84 to 0.62 degrees C without a significant increase in pressure drop.
The holistic implementation of production approaches helps producing companies (PCs) to overcome challenges that threaten their competitiveness. By implementing lean, digital, and sustainable measures holistically and in a target-oriented manner, PCs can exploit synergies and ensure the long-term viability of their production systems (PSs), even in volatile environments. A prerequisite for this is understanding how the different elements of the system interact. This article contributes to this understanding by elaborating these interdependencies in future-proof PSs. Additionally, it identifies synergy potentials to determine the most significant leverage effects (LVs). Two Delphi studies were conducted, followed by a literature analysis to verify the findings scientifically. The results show several synergies that can make PSs leaner, more digital and more sustainable when the paradigms are considered holistically. However, the results also reveal that, implementing or improving lean and digitalisation measures has greater synergy potential for sustainability than reverse. Moreover, especially quality-oriented lean elements have broad LVs within the sustainability and digitalisation paradigm. Implementing quality-oriented lean elements can therefore improve the sustainability and digitalisation progress of a PS, thereby increasing the competitiveness of PCs. Thus, PCs can use the derived interdependencies to select the appropriate measures to achieve a future-proof PS, considering the LVs.
